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Cloud, Edge, or Both? Towards Decision Support for Designing IoT Applications
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0003-0998-6585
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0003-0326-0556
2018 (engelsk)Inngår i: 2018 Fifth International Conference on Internet of Things: Systems, Management and Security, 2018Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

The rapidly evolving Internet of Things (IoT) includes applications which might generate a huge amount of data, this requires appropriate platforms and support methods. Cloud computing offers attractive computational and storage solutions to cope with these issues. However, sending to centralized servers all the data generated at the edge of the network causes latency, energy consumption, and high bandwidth demand. Performing some computations at the edge of the network, known as Edge computing, and using a hybrid Edge-Cloud architecture can help addressing these challenges. While such architecture may provide new opportunities to distribute IoT applications, making optimal decisions regarding where to deploy the different application components is not an easy and straightforward task for designers. Supporting designers’ decisions by considering key quality attributes impacting them in an Edge-Cloud architecture has not been investigated yet. In this paper, we: explore the importance of decision support for the designers, discuss how different attributes impact the decisions, and describe the required steps toward a decision support framework for IoT application designers.

sted, utgiver, år, opplag, sider
2018.
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-16827DOI: 10.1109/IoTSMS.2018.8554827ISI: 000455671800023Scopus ID: 2-s2.0-85059973173Lokal ID: 26740ISBN: 978-1-5386-9585-2 (digital)OAI: oai:DiVA.org:mau-16827DiVA, id: diva2:1420341
Konferanse
The Fifth International Conference on Internet of Things: Systems, Management and Security (IoTSMS 2018), Valencia, Spain (15-18 Oct 2018)
Tilgjengelig fra: 2020-03-30 Laget: 2020-03-30 Sist oppdatert: 2023-12-28bibliografisk kontrollert
Inngår i avhandling
1. Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
Åpne denne publikasjonen i ny fane eller vindu >>Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
2021 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision.

To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation.

sted, utgiver, år, opplag, sider
Malmö: Malmö universitet, 2021. s. 141
Serie
Studies in Computer Science ; 13
Emneord
Internet of Things, Edge computing, Decision Support, Quality Attrib-utes, Metrics, Simulation
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-37068 (URN)10.24834/isbn.9789178771592 (DOI)978-91-7877-158-5 (ISBN)978-91-7877-159-2 (ISBN)
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Tilgjengelig fra: 2020-12-03 Laget: 2020-12-02 Sist oppdatert: 2024-03-07bibliografisk kontrollert

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